Who we are

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?
In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.
This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications.
Learning Outcomes: By the end of this course, you will be able to:
-Identify potential applications of machine learning in practice.
-Describe the core differences in analyses enabled by regression, classification, and clustering.
-Select the appropriate machine learning task for a potential application.
-Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
-Represent your data as features to serve as input to machine learning models.
-Assess the model quality in terms of relevant error metrics for each task.
-Utilize a dataset to fit a model to analyze new data.
-Build an end-to-end application that uses machine learning at its core.
-Implement these techniques in Python.

審閱

SZ

Great course!\n\nEmily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.

MK

Jul 21, 2019

Filled StarFilled StarFilled StarFilled StarFilled Star

A great course, really designed to understand the underlying core concepts of machine learning using real-life examples which takes you through all that with little to no programming skills required!

從本節課中

Welcome

Machine learning is everywhere, but is often operating behind the scenes. <p>This introduction to the specialization provides you with insights into the power of machine learning, and the multitude of intelligent applications you personally will be able to develop and deploy upon completion.</p>We also discuss who we are, how we got here, and our view of the future of intelligent applications.

教學方

Emily Fox

Carlos Guestrin

腳本

[MUSIC] Hi. I'm Carlos Guestrin. I'm the Amazon Professor of Machine Learning. >> Wait, I'm the Amazon Professor of Machine Learning. >> Who are you? >> I'm Emily Fox. >> Well, I'm the Amazon Professor of Machine Learning in Computer Science. >> And I'm the Amazon Professor of Machine Learning in Statistics. >> We're both at the University of Washington in Seattle, this is the United States. [LAUGH] This is. >> This is the United States of America. >> Of America. >> To clarify. >> America. >> America. >> [LAUGH] So what are your qualifications to be here today, Emily? [LAUGH] >> Am I supposed to read my CV? No, okay, just the beginning of it. What are my qualifications? Okay. Well, I went to MIT for way too long, nine years, though it wasn't all for one degree, got a couple of them [LAUGH] during those nine years. But yeah, I was there for my PhD studying in electrical engineering and computer science. Should I list more qualifications? I think that's pretty good. >> She's very well qualified. Let me just say that. [LAUGH] And I got my undergrad from University of Sao Paulo in Brazil. And my PhD from Stanford in computer science and I've been working in machine learning for a long time, as Emily likes to say. >> A very long time. I guess maybe I should describe a little bit about my transition to statistics. So, after I did my PhD in electrical engineering and computer science, I did a postdoc in statistics at Duke, and actually, honestly, a lot of my PhD really was a statistics PhD. But MIT, as of the time of this recording, does not have a statistics department, and did not at the time I studied there so, yes, I'm a self-taught statistician. >> And we can all be self taught. >> Yes. >> [LAUGH] >> That's what this MOOC is about. >> [LAUGH] Hey, we're learning together. So I've been working machine learning for awhile and I'm really excited about methods in machine learning from a wide range of settings. I've worked on theoretical algorithms for what's called planning under uncertainty for making decisions using machine learning. I've worked with sensor data with distributed sensors, something called sensor networks which today is called Internet of Things. But this is way back and then I took a big focus on large-scale machine learning. So how do you scale up machine learning methods to lots and lots and lots of data? And we'll use some of these ideas in this specialization as well. How about you? >> I don't have quite as long of a history as Carlos does. [LAUGH] But the things that I really enjoy working on are time series applications, the data that comes in over time and we want to find some underlying structure or patterns to that data. And a lot of my focus in my research is on how to scale up to really high dimensional or large collections of time series, especially when they have very complex dynamics. And another one of my big interests is how to make inferences online as data is streaming in. And I've worked on a wide range of different applications, some that I'm really passionate about. One in particular, right now, is neuroscience, but I've also worked on speech data, motion capture applications, analyzing human motion, to financial data and the list goes on and on. But lots of data have really important temporal structure and that's really a passion of mine. >> Yeah. >> No, yeah, that's it. >> And we've been teaching machine learning for a long time and maybe in the university setting- >> Some of us longer than others. >> This joke never ends. >> No. >> And many at the university setting. So here we are, it's an exciting experience for us to teach in this online course. >> Yes. >> So how did you decide to do this online course? >> How did I decide? That's a very good question. Well, no, there are a lot of reasons why I wanted to do this online course. But really, the most fundamental reason was thinking about how to disseminate machine learning to a very broad audience, because personally, I came from a very different background and training. And actually Carlos did as well and so I, myself, have gone through this experience of trying to learn machine learning, learn statistics. And I think there's a really nice way that it can be taught at a very fundamental level to a very broad audience and that's one of my passions. And another reason is I'd like to think about this for other goals in the future of how to convey this to other groups that I interact with on campus as a teacher at the University of Washington, professor [LAUGH], assistant. [LAUGH] >> Not for long. So, yeah, and I'm very excited about how machine learning gets taught. And we really have been thinking a lot about how to approach this really broad idea of machine learning in a new way. So in this specialization, as we'll talk more about in this module, we'll experience machine learning in a whole different way than you experienced in other places. We're really gonna be focused on for applications first, understanding how machine learning has impact and then digging in and understanding how those methods are built and how they can be useful. So part of the journey for us is to build a new way to teach machine learning, which is cool. >> It is cool. >> So we're excited. >> Yes. We're excited. [LAUGH] [MUSIC]